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Information Journal Paper

Title

DEVELOPING AN INTELLIGENT HYBRID MODEL FOR PREDICTING TEHRAN'S STOCK EXCHANGE

Pages

  35-49

Keywords

ARTIFICIAL NEURAL NETWORK (ANN)Q2
BAT ALGORITHM (BA)Q2

Abstract

 Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who have access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in an intelligent framework called bat-neural network (BNN) to predict stock price. The capability of BNNMAS evaluated by applying on two sets of TEHRAN STOCK EXCHANGE data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like Generalized Regression Neural Network (GRNN). The results show that BNN significantly performs better than others, so it can be considered as a suitable tool for predicting stock price especially in a long term periods.

Cites

References

Cite

APA: Copy

HAFEZI, R., SHAHRABI, J., & HADAVANDI, E.. (2013). DEVELOPING AN INTELLIGENT HYBRID MODEL FOR PREDICTING TEHRAN'S STOCK EXCHANGE. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), 10(2 (37)), 35-49. SID. https://sid.ir/paper/164669/en

Vancouver: Copy

HAFEZI R., SHAHRABI J., HADAVANDI E.. DEVELOPING AN INTELLIGENT HYBRID MODEL FOR PREDICTING TEHRAN'S STOCK EXCHANGE. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS)[Internet]. 2013;10(2 (37)):35-49. Available from: https://sid.ir/paper/164669/en

IEEE: Copy

R. HAFEZI, J. SHAHRABI, and E. HADAVANDI, “DEVELOPING AN INTELLIGENT HYBRID MODEL FOR PREDICTING TEHRAN'S STOCK EXCHANGE,” JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), vol. 10, no. 2 (37), pp. 35–49, 2013, [Online]. Available: https://sid.ir/paper/164669/en

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